In deep learning, gradient descent often gets stuck in local optima. While these points may not be perfect, they represent workable solutions given the current landscape. Similarly, existing “wheels” in software development are like these local optima—well-established solutions that work well in many cases but might not fit every unique scenario.
Reinventing the wheel can be seen as an attempt to escape these local optima, exploring new approaches that better suit specific needs or constraints. This process involves effort and risk, just like tweaking optimization parameters in training, but it can lead to innovative and more tailored solutions.
So, rather than blindly accepting existing solutions, sometimes it’s worth challenging them and trying to find a different path—just as in deep learning, where escaping local optima can improve model performance.
Reinventing the wheel can be seen as an attempt to escape these local optima, exploring new approaches that better suit specific needs or constraints. This process involves effort and risk, just like tweaking optimization parameters in training, but it can lead to innovative and more tailored solutions.
So, rather than blindly accepting existing solutions, sometimes it’s worth challenging them and trying to find a different path—just as in deep learning, where escaping local optima can improve model performance.